The Office of Research Cyberinfrastructure is hosting a one-day Research Computing Advanced Bootcamp for users interested in specialized topics in research computing such as strategies for leveraging tools for understanding system profiling, limitations of pandas for large DataFrames, other high-performance tools for DataFrames, querying large language models via Python APIs, reproducibility practices, and automated plotting techniques. The workshop will include three sessions featuring hands-on exercises, followed by an open discussion and Q&A.
Session 1: Handling Large DataFrames in Python
This session explores the performance and memory limitations of pandas when working with large-scale datasets.
It presents modern alternatives such as Polars and covers efficient data handling techniques, including optimized storage formats, chunked processing, and extensions to distributed and GPU-enabled frameworks.
Session 2: Python and DataFrames for Sensible Experiment Management
This session focuses on developing structured and reproducible workflows for computational research.
Participants will build a benchmarking framework for LLM inference while learning best practices in data aggregation, API integration, and automated visualization.
For further information on sessions and tentative agenda please visit:
https://rci.research.ucf.edu/events/research-computing-full-day-advanced-bootcamp-june-2026/
Please note: All the sessions have a hands-on component. To participate in the hands-on exercises during the session, you will need to bring your own laptop equipped with a web browser as well as install any software specific to that lesson. Refer to each lesson's description for specific instruction.
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